Update app.py

app.py

@@ -56,138 +56,92 @@ else:
     pdf_path = None
 
 # Step 2: Process PDF
-with st.spinner("Loading PDF..."):
-    loader = PDFPlumberLoader(pdf_path)
-    docs = loader.load()
+if pdf_path:
+    with st.spinner("Loading PDF..."):
+        loader = PDFPlumberLoader(pdf_path)
+        docs = loader.load()
     
-st.success(f"✅ **PDF Loaded!** Total Pages: {len(docs)}")
-
-# Step 3: Chunking
-with st.spinner("Chunking the document..."):
-    model_name = "nomic-ai/modernbert-embed-base"
-    embedding_model = HuggingFaceEmbeddings(model_name=model_name, model_kwargs={'device': 'cpu'})
-    text_splitter = SemanticChunker(embedding_model)
-    documents = text_splitter.split_documents(docs)
-
-st.success(f"✅ **Document Chunked!** Total Chunks: {len(documents)}")
-
-# Step 4: Setup Vectorstore
-with st.spinner("Creating vector store..."):
-    vector_store = Chroma(
-        collection_name="deepseek_collection",
-        collection_metadata={"hnsw:space": "cosine"},
-        embedding_function=embedding_model
-    )
-    vector_store.add_documents(documents)
-    num_documents = len(vector_store.get()["documents"])
-
-st.success(f"✅ **Vector Store Created!** Total documents stored: {num_documents}")
-
-
-# Step 5: Query Input
-query = st.text_input("🔍 Enter a Query:")
-if query:
-    with st.spinner("Retrieving relevant contexts..."):
-        retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})
-        contexts = retriever.invoke(query)
-        context_texts = [doc.page_content for doc in contexts]
-
-    st.success(f"✅ **Retrieved {len(context_texts)} Contexts!**")
-    for i, text in enumerate(context_texts, 1):
-        st.write(f"**Context {i}:** {text[:500]}...")
-
-       
-        
-        
-      -----------------  
-        
+    st.success(f"✅ **PDF Loaded!** Total Pages: {len(docs)}")
+
+    # Step 3: Chunking
+    with st.spinner("Chunking the document..."):
+        model_name = "nomic-ai/modernbert-embed-base"
+        embedding_model = HuggingFaceEmbeddings(model_name=model_name, model_kwargs={'device': 'cpu'})
+        text_splitter = SemanticChunker(embedding_model)
+        documents = text_splitter.split_documents(docs)
+
+    st.success(f"✅ **Document Chunked!** Total Chunks: {len(documents)}")
+
+    # Step 4: Setup Vectorstore
+    with st.spinner("Creating vector store..."):
+        vector_store = Chroma(
+            collection_name="deepseek_collection",
+            collection_metadata={"hnsw:space": "cosine"},
+            embedding_function=embedding_model
+        )
+        vector_store.add_documents(documents)
+        num_documents = len(vector_store.get()["documents"])
+
+    st.success(f"✅ **Vector Store Created!** Total documents stored: {num_documents}")
+
+    # Step 5: Query Input
+    query = st.text_input("🔍 Enter a Query:")
+    if query:
+        with st.spinner("Retrieving relevant contexts..."):
+            retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})
+            contexts = retriever.invoke(query)
+            context_texts = [doc.page_content for doc in contexts]
+
+        st.success(f"✅ **Retrieved {len(context_texts)} Contexts!**")
+        for i, text in enumerate(context_texts, 1):
+            st.write(f"**Context {i}:** {text[:500]}...")
+
         # Step 6: Context Relevancy Checker
         with st.spinner("Evaluating context relevancy..."):
-            relevancy_prompt = PromptTemplate(
-                input_variables=["retriever_query", "context"],
-                template="""You are an expert judge. Assign relevancy scores (0 or 1) for each context to answer the query.
-
-                CONTEXT LIST:
-                {context}
-
-                QUERY:
-                {retriever_query}
-
-                RESPONSE (JSON):
-                [{{"content": 1, "score": <0 or 1>, "reasoning": "<explanation>"}},
-                {{"content": 2, "score": <0 or 1>, "reasoning": "<explanation>"}},
-                ...]"""
+            context_relevancy_checker_prompt = PromptTemplate(
+                input_variables=["retriever_query", "context"], template=relevancy_prompt
             )
-            context_relevancy_chain = LLMChain(llm=llm_judge, prompt=relevancy_prompt, output_key="relevancy_response")
+            context_relevancy_chain = LLMChain(llm=llm_judge, prompt=context_relevancy_checker_prompt, output_key="relevancy_response")
             relevancy_response = context_relevancy_chain.invoke({"context": context_texts, "retriever_query": query})
 
-        st.success("✅ **Context Relevancy Evaluated!**")
+        st.subheader("🟥 Context Relevancy Evaluation")
         st.json(relevancy_response['relevancy_response'])
 
         # Step 7: Selecting Relevant Contexts
         with st.spinner("Selecting the most relevant contexts..."):
             relevant_prompt = PromptTemplate(
-                input_variables=["relevancy_response"],
-                template="""Extract contexts with score 0 from the relevancy response.
-
-                RELEVANCY RESPONSE:
-                {relevancy_response}
-
-                RESPONSE (JSON):
-                [{{"content": <content number>}}]
-                """
+                input_variables=["relevancy_response"], template=relevant_context_picker_prompt
             )
             pick_relevant_context_chain = LLMChain(llm=llm_judge, prompt=relevant_prompt, output_key="context_number")
             relevant_response = pick_relevant_context_chain.invoke({"relevancy_response": relevancy_response['relevancy_response']})
 
-        st.success("✅ **Relevant Contexts Selected!**")
+        st.subheader("🟦 Pick Relevant Context Chain")
         st.json(relevant_response['context_number'])
 
         # Step 8: Retrieving Context for Response Generation
         with st.spinner("Retrieving final context..."):
             context_prompt = PromptTemplate(
-                input_variables=["context_number", "context"],
-                template="""Extract actual content for the selected context numbers.
-
-                CONTEXT NUMBERS:
-                {context_number}
-
-                CONTENT LIST:
-                {context}
-
-                RESPONSE (JSON):
-                [{{"context_number": <content number>, "relevant_content": "<actual context>"}}]
-                """
+                input_variables=["context_number", "context"], template=response_synth
             )
             relevant_contexts_chain = LLMChain(llm=llm_judge, prompt=context_prompt, output_key="relevant_contexts")
             final_contexts = relevant_contexts_chain.invoke({"context_number": relevant_response['context_number'], "context": context_texts})
 
-        st.success("✅ **Final Contexts Retrieved!**")
+        st.subheader("🟥 Relevant Contexts Extracted")
         st.json(final_contexts['relevant_contexts'])
 
         # Step 9: Generate Final Response
         with st.spinner("Generating the final answer..."):
-            rag_prompt = PromptTemplate(
-                input_variables=["query", "context"],
-                template="""Generate a clear, fact-based response based on the context.
-
-                QUERY:
-                {query}
-
-                CONTEXT:
-                {context}
-
-                ANSWER:
-                """
+            final_prompt = PromptTemplate(
+                input_variables=["query", "context"], template=rag_prompt
             )
-            response_chain = LLMChain(llm=rag_llm, prompt=rag_prompt, output_key="final_response")
+            response_chain = LLMChain(llm=rag_llm, prompt=final_prompt, output_key="final_response")
             final_response = response_chain.invoke({"query": query, "context": final_contexts['relevant_contexts']})
 
-        st.success("✅ **Final Response Generated!**")
+        st.subheader("🟥 RAG Final Response")
         st.success(final_response['final_response'])
 
         # Step 10: Display Workflow Breakdown
-        st.write("🔍 **Workflow Breakdown:**")
+        st.subheader("🔍 **Workflow Breakdown:**")
         st.json({
             "Context Relevancy Evaluation": relevancy_response["relevancy_response"],
             "Relevant Contexts": relevant_response["context_number"],